Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

1 Scopus Citations
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Author(s)

  • Zixuan Yuan
  • Hao Liu
  • Yanchi Liu
  • Yang Yang
  • Renjun Hu
  • Hui Xiong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationThe Web Conference 2021
Subtitle of host publicationProceedings of The World Wide Web Conference WWW 2021
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages1586–1597
ISBN (Electronic)9781450383127
Publication statusPublished - Apr 2021

Publication series

NameThe Web Conference - Proceedings of the World Wide Web Conference, WWW

Conference

Title30th Web Conference 2021 (WWW 2021)
LocationVirtual
PlaceSlovenia
CityLjubljana
Period19 - 23 April 2021

Link(s)

Abstract

Query and Point-of-Interest (POI) matching, aiming at recommending the most relevant POIs from partial query keywords, has become one of the most essential functions in online navigation and ride-hailing applications. Existing methods for query-POI matching, such as Google Maps and Uber, have a natural focus on measuring the static semantic similarity between contextual information of queries and geographical information of POIs. However, it remains challenging for dynamic and personalized online query-POI matching because of the non-stationary and situational context-dependent query-POI relevance. Moreover, the large volume of online queries requires an adaptive and incremental model training strategy that is efficient and scalable in the online scenario. To this end, in this paper, we propose an Incremental Spatio-Temporal Graph Learning (IncreSTGL) framework for intelligent online query-POI matching. Specifically, we first model dynamic query-POI interactions as microscopic and macroscopic graphs. Then, we propose an incremental graph representation learning module to refine and update query-POI interaction graphs in an online incremental fashion, which includes: (i) a contextual graph attention operation quantifying query-POI correlation based on historical queries under dynamic situational context, (ii) a graph discrimination operation capturing the sequential query-POI relevance drift from a holistic view of personalized preference and social homophily, and (iii) a multi-level temporal attention operation summarizing the temporal variations of query-POI interaction graphs for subsequent query-POI matching. Finally, we introduce a lightweight semantic matching module for online query-POI similarity measurement. To demonstrate the effectiveness and efficiency of the proposed algorithm, we conduct extensive experiments on two real-world datasets collected from a leading online navigation and map service provider in China.

Research Area(s)

  • Query-POI Matching, Spatio-Temporal Analysis, Incremental Graph Learning, User Modeling

Citation Format(s)

Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching. / Yuan, Zixuan; Liu, Hao; Liu, Junming et al.

The Web Conference 2021: Proceedings of The World Wide Web Conference WWW 2021. New York : Association for Computing Machinery, 2021. p. 1586–1597 (The Web Conference - Proceedings of the World Wide Web Conference, WWW).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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